Cognitive Offloading to AI: It's Not All Bad!

New research shows how experts (like you) can use AI to reason better.

New Research on Cognitive Offloading

It has been widely reported that using AI makes thinking both easier and worse. Easier because AI does the majority of the thinking for you and worse because … AI does the majority of the thinking for you.

One lynchpin in this argument is the 2024 study by Stadler, Bannert and Sailer, in which university students were randomly assigned to research a scientific question using either ChatGPT or Google. The ChatGPT group reported significantly lower cognitive load. They also produced lower-quality reasoning.

This result, or others like it, may have led you to reevaluate your own use of AI, or if you work in the area of education, seriously question its use as a pedagogical tool. But there is a follow-up by those same researchers. A recently published preprint by Stadler et al. casts doubt on the claim that offloading some of the work to AI is always a bad idea. The key variable that they added to the mix: domain expertise.

Medical students and social science students were both asked to research the safety of nanoparticle-based sunscreens, using either ChatGPT-4 or Google. The finding: AI reduced cognitive load for everyone, regardless of background. But the quality bit of the equation reversed. Social science students, non-experts on this medical question, produced worse reasoning with ChatGPT, just as the original study would predict. Medical students, who had relevant domain knowledge, produced better reasoning with ChatGPT than with Google.

Now before discussing what these new results portend for critical thinking, yours and others’, it is worth underlining that this study is a preprint, not a peer-reviewed journal article. The findings, however, support what many of us find: working judiciously with chatbots, with intention and discernment, enhances the quality of our thinking, quietly amplifying our strengths while shoring up weaknesses.

Below I run through the research and then offer some concrete advice on how to confidently use AI when you know what you are talking about.

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Who and How Matters with AI

If you aren’t familiar with cognitive offloading, it is formally defined in a 2016 paper by Risko and Gilbert as: the use of physical action to alter the information processing requirements of a task in order to reduce cognitive demand. There are three types of cognitive load: intrinsic, which is the inherent difficulty of the content; extrinsic, or how the information is presented; and germane, which is what most people think of when discussing this concept, which is the effort it takes to make what we are engaging with mean something.

Research into cognitive offloading to AI is ongoing, but so far, the consensus is mostly negative. Here is a quick rundown of the bad news.

AI reduces the effort you invest in thinking. The original Stadler, Bannert and Sailer (2024) study showed this with undergraduates. A 2025 Microsoft Research / Carnegie Mellon survey of 319 knowledge workers found the same pattern in real jobs across industries: GenAI shifts work away from active problem-solving toward passive verification and oversight.

That effort reduction comes at a cost. A randomised controlled trial with nearly 1,000 high school maths students gave some unrestricted access to GPT-4 during practice sessions. Those students performed 48% better with AI, then 17% worse on the subsequent unassisted exam.

People misjudge which tasks are safe to hand off. An experiment with 758 management consultants at Boston Consulting Group found that AI improved performance on tasks within the (known) capability range of the technology. But for tasks that looked similar yet fell outside AI’s capabilities, consultants using AI performed 19 percentage points worse than those working without it.

Confidence in AI predicts less critical thinking. That Microsoft Research / Carnegie Mellon survey also found that people who trusted AI highly were less likely to question its output. In parallel, it also found that people with high self-confidence in their own domain applied more critical thinking. In other words, the danger here is that you are most likely to accept AI output exactly at the moment you shouldn’t: when you know the least.

In a nutshell, it doesn’t look good for the co-thinking movement, those like me who believe that partnering with AI can augment your ability to think, decide, and problem-solve.

The Power of Knowing Something

Stadler et al.’s preprint doesn’t refute any of the claims above; it adds complexity.

They found that when the social science students used ChatGPT, they had no framework to evaluate what it returned. (Without a doubt, an experience that we’ve all had!) The AI retrieved the information, but is not a reliable judge, nor in this case, are the students. Contrast that with our medical friends. When they used ChatGPT, they had concrete, hard-won knowledge to draw on. The AI handled the retrieval, while they handled the reasoning. And importantly for the rest of us — the results of their work were better and produced with less effort.

Here is what the authors of the study conclude:

The results confirm and expand upon the earlier findings. Across both groups, LLM users reported significantly lower levels of cognitive load (intrinsic, extraneous and germane), replicating the 'ease' effect identified by Stadler et al. (2024). However, the quality of justifications revealed a more nuanced picture: whereas social sciences students produced better arguments using search engines, medical students showed the opposite pattern, generating stronger justifications with the LLM. This interaction effect suggests that prior knowledge moderates whether the cognitive ease afforded by an LLM comes at the expense of epistemic performance or enables more efficient reasoning without compromising depth.

We can see how this new result fits the growing picture of how to use AI effectively as a reasoning partner. The problem isn’t AI, it’s passively using AI. When you know a subject, you have frameworks to think with and standards to check against. You are a better partner for AI because you bring something to the partnership.

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Putting AI to Work

Pulling together both the negative results of cognitive offloading with the silver lining of Stadler et al.’s preprint, a usable, consistent picture of best practice is emerging.

Use AI for retrieval and generation, not for evaluation and judgment. The consistent finding is that offloading production is less damaging than offloading evaluation.

Be strategic. This is the flipside of the previous principle. When using the technology, think strategically about how you will extract value. Use your time to ask the right questions, evaluate the reliability of the outputs, and do the hard work of weaving everything together into a coherent, credible explanation.

Decide the structure before you delegate the content. The BCG study found that consultants who clearly divided tasks between themselves and AI outperformed those who integrated AI into every step. Structural thinking is a cognitive act that is enhanced by expertise — don’t delegate it away!

Be most skeptical where you know the least. This is so hard but a hugely important lesson when working with AI. The less you know about a subject, the more carefully you should question what AI tells you about it.

Be confident, just not too confident. Every study that measures self-perception alongside actual performance finds the same thing: people think AI is helping them more than it is.

The challenge ahead, as I see it, is not to avoid AI but to learn how to bring enough of yourself to the partnership. The new preprint offers a preliminary but encouraging data point: when you know your subject, AI can help you produce smarter, better arguments.